remove legacy adapt

aeon/classification/compose/tests/test_pipeline.py

@@ -17,7 +17,7 @@
 )
 from aeon.testing.mock_estimators import MockCollectionTransformer
 from aeon.testing.utils.estimator_checks import _assert_array_almost_equal
-from aeon.transformations.adapt import TabularToSeriesAdaptor
+from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,

aeon/clustering/compose/tests/test_pipeline.py

@@ -15,7 +15,7 @@
 )
 from aeon.testing.mock_estimators import MockCollectionTransformer
 from aeon.testing.utils.estimator_checks import _assert_array_almost_equal
-from aeon.transformations.adapt import TabularToSeriesAdaptor
+from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,

aeon/forecasting/base/_base.py

@@ -124,7 +124,7 @@ def __mul__(self, other):
             not nested, contains only non-TransformerPipeline `aeon` transformers
         """
         from aeon.forecasting.compose import TransformedTargetForecaster
-        from aeon.transformations.adapt import TabularToSeriesAdaptor
+        from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
         from aeon.transformations.base import BaseTransformer
         from aeon.utils.sklearn import is_sklearn_transformer
 
@@ -155,7 +155,7 @@ def __rmul__(self, other):
             not nested, contains only non-TransformerPipeline `aeon` steps
         """
         from aeon.forecasting.compose import TransformedTargetForecaster
-        from aeon.transformations.adapt import TabularToSeriesAdaptor
+        from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
         from aeon.transformations.base import BaseTransformer
         from aeon.utils.sklearn import is_sklearn_transformer
 
@@ -186,7 +186,7 @@ def __rpow__(self, other):
             not nested, contains only non-TransformerPipeline `aeon` steps
         """
         from aeon.forecasting.compose import ForecastingPipeline
-        from aeon.transformations.adapt import TabularToSeriesAdaptor
+        from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
         from aeon.transformations.base import BaseTransformer
         from aeon.utils.sklearn import is_sklearn_transformer
 

aeon/forecasting/compose/_pipeline.py

@@ -240,7 +240,7 @@ def get_test_params(cls, parameter_set="default"):
         from aeon.forecasting.compose._reduce import DirectReductionForecaster
         from aeon.forecasting.naive import NaiveForecaster
         from aeon.testing.mock_estimators import MockTransformer
-        from aeon.transformations.adapt import TabularToSeriesAdaptor
+        from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
         from aeon.transformations.detrend import Detrender
 
         # StandardScaler does not skip fit, NaiveForecaster is not probabilistic

aeon/forecasting/compose/tests/test_pipeline.py

@@ -18,7 +18,7 @@
 from aeon.testing.data_generation._legacy import get_examples, make_series
 from aeon.testing.mock_estimators import MockForecaster, MockTransformer
 from aeon.testing.utils.estimator_checks import _assert_array_almost_equal
-from aeon.transformations.adapt import TabularToSeriesAdaptor
+from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
 from aeon.transformations.detrend import Detrender
 from aeon.transformations.hierarchical.aggregate import Aggregator
 from aeon.transformations.impute import Imputer

aeon/regression/compose/tests/test_pipeline.py

@@ -17,7 +17,7 @@
 )
 from aeon.testing.mock_estimators import MockCollectionTransformer
 from aeon.testing.utils.estimator_checks import _assert_array_almost_equal
-from aeon.transformations.adapt import TabularToSeriesAdaptor
+from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,

aeon/transformations/adapt.py → aeon/transformations/_legacy/adapt.py

@@ -224,142 +224,3 @@ def get_test_params(cls, parameter_set="default"):
         params2 = {"transformer": StandardScaler(), "fit_in_transform": True}
 
         return [params1, params2]
-
-
-# TODO: remove in v0.11.0
-@deprecated(
-    version="0.10.0",
-    reason="PandasTransformAdaptor will be removed in version 0.11.0.",
-    category=FutureWarning,
-)
-class PandasTransformAdaptor(BaseTransformer):
-    """
-    Adapt pandas transformations to aeon interface.
-
-    In `transform`, executes `pd.DataFrame` method of name `method` on data,
-    optionally with keywords arguments passed, via `kwargs` hyper-parameter.
-    The `apply_to` parameter controls what the data is upon which `method` is called:
-    "call" = for `X` seen in `transform`, "all"/"all_subset" = all data seen so far.
-    See below for details.
-
-    For hierarchical series, operation is applied by instance.
-
-    Parameters
-    ----------
-    method : str, optional, default = None = identity transform
-        Name of the method of DataFrame that is applied in transform.
-    kwargs : dict, optional, default = empty dict (no kwargs passed to method)
-        Arguments passed to DataFrame.method.
-    apply_to : str, one of "call", "all", "all_subset", optional, default = "call"
-        "call" = method is applied to `X` seen in transform only
-        "all" = method is applied to all `X` seen in `fit`, `update`, `transform`
-            more precisely, the application to `self._X` is returned
-        "all_subset" = method is applied to all `X` like for "all" value,
-            but before returning, result is sub-set to indices of `X` in `transform`
-        in "all", "all_subset", `X` seen in `transform` do not update `self._X`.
-    """
-
-    _tags = {
-        "input_data_type": "Series",
-        # what is the abstract type of X: Series, or Panel
-        "output_data_type": "Series",
-        # what abstract type is returned: Primitives, Series, Panel
-        "instancewise": True,
-        "X_inner_type": "pd.DataFrame",
-        "y_inner_type": "None",
-        "capability:multivariate": True,
-        "transform-returns-same-time-index": False,
-        "fit_is_empty": False,
-        "capability:inverse_transform": False,
-        "remember_data": False,
-    }
-
-    def __init__(self, method, kwargs=None, apply_to="call"):
-        self.method = method
-        self.kwargs = kwargs
-        self.apply_to = apply_to
-
-        if not isinstance(apply_to, str):
-            raise TypeError(
-                f"apply_to parameter must be a str, but found {type(apply_to)}"
-            )
-        if apply_to not in ["call", "all", "all_subset"]:
-            raise ValueError(
-                'apply_to must be one of "call", "all", "all_subset", '
-                f'but found "{apply_to}"'
-            )
-
-        super().__init__()
-
-        if apply_to in ["all", "all_subset"]:
-            self.set_tags(**{"remember_data": True})
-
-        if apply_to == "all_subset":
-            self.set_tags(**{"transform-returns-same-time-index": True})
-
-        if apply_to == "call":
-            self.set_tags(**{"fit_is_empty": True})
-
-    def _transform(self, X, y=None):
-        """Transform X and return a transformed version.
-
-        private _transform containing the core logic, called from transform
-
-        Parameters
-        ----------
-        X : pd.DataFrame
-            Data to be transformed
-        y : ignored argument for interface compatibility
-            Additional data, e.g., labels for transformation
-
-        Returns
-        -------
-        Xt : pd.DataFrame
-            transformed version of X
-        """
-        apply_to = self.apply_to
-        method = self.method
-        kwargs = self.kwargs
-
-        if kwargs is None:
-            kwargs = {}
-
-        if apply_to in ["all", "all_subset"]:
-            _X = X.combine_first(self._X)
-        else:
-            _X = X
-
-        Xt = getattr(_X, method)(**kwargs)
-
-        if apply_to in ["all_subset"]:
-            Xt = Xt.loc[X.index]
-
-        return Xt
-
-    @classmethod
-    def get_test_params(cls, parameter_set="default"):
-        """Return testing parameter settings for the estimator.
-
-        Parameters
-        ----------
-        parameter_set : str, default="default"
-            Name of the set of test parameters to return, for use in tests. If no
-            special parameters are defined for a value, will return `"default"` set.
-
-        Returns
-        -------
-        params : dict or list of dict, default = {}
-            Parameters to create testing instances of the class
-            Each dict are parameters to construct an "interesting" test instance, i.e.,
-            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
-        """
-        params1 = {"method": "diff"}
-        params2 = {"method": "diff", "kwargs": {"periods": 2}, "apply_to": "all_subset"}
-        params3 = {
-            "method": "shift",
-            "kwargs": {"periods": 12},
-            "apply_to": "all",
-        }
-
-        return [params1, params2, params3]

aeon/transformations/collection/compose/tests/test_pipeline.py

@@ -11,7 +11,7 @@
 )
 from aeon.testing.mock_estimators import MockCollectionTransformer
 from aeon.testing.utils.estimator_checks import _assert_array_almost_equal
-from aeon.transformations.adapt import TabularToSeriesAdaptor
+from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
 from aeon.transformations.collection import (
     AutocorrelationFunctionTransformer,
     HOG1DTransformer,

aeon/transformations/compose.py

@@ -34,7 +34,7 @@
 
 def _coerce_to_aeon(other):
     """Check and format inputs to dunders for compose."""
-    from aeon.transformations.adapt import TabularToSeriesAdaptor
+    from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
 
     # if sklearn transformer, adapt to aeon transformer first
     if is_sklearn_transformer(other):

aeon/transformations/tests/test_optionalpassthrough.py

@@ -23,7 +23,7 @@ def test_optionalpassthrough():
         SlidingWindowSplitter,
     )
     from aeon.forecasting.naive import NaiveForecaster
-    from aeon.transformations.adapt import TabularToSeriesAdaptor
+    from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
     from aeon.transformations.compose import OptionalPassthrough
     from aeon.transformations.detrend import Deseasonalizer
 

aeon/transformations/tests/test_pipeline.py

@@ -7,7 +7,7 @@
 from sklearn.tree import DecisionTreeClassifier
 
 from aeon.testing.data_generation import make_example_nested_dataframe
-from aeon.transformations.adapt import TabularToSeriesAdaptor
+from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
 from aeon.transformations.collection.segment import RandomIntervalSegmenter
 
 # load data

aeon/transformations/tests/test_sklearn_adaptor.py

@@ -7,7 +7,7 @@
 from sklearn.preprocessing import PowerTransformer
 
 from aeon.datasets import load_airline
-from aeon.transformations.adapt import TabularToSeriesAdaptor
+from aeon.transformations._legacy.adapt import TabularToSeriesAdaptor
 
 
 def test_boxcox_transform():